Field
The present innovations relate generally to traffic control, and, more, specifically, to systems and method involving adaptive and/or autonomous traffic control.
Description of Related Information
Neural network technologies have been in development for decades where real-time computing problems are solved by using software or circuitry which emulates the brain's function. The human brain contains roughly 100 billion neurons. About 300 million neurons are dedicated to the visual cortex, just one of several sensory input sources to the brain. Small to medium scale artificial intelligence systems using neural network technology have been used successfully in many real-world applications such as pattern recognition for industrial process sorting or quality control functions and real-time navigation and collision avoidance systems, with results meeting or exceeding human capabilities. For example, challenges sponsored by DARPA have taken place where autonomous vehicles employing neural network technology have successfully navigated across vast distances of hazardous desert terrain or through urban courses.
Human brains and artificial neural networks both store memorized visual images, other sensory input, and related sequences thereof to make real-time predictions, i.e. decisions, from those sensory inputs. As with other well-adapted animal species having brains smaller than those of humans, artificial neural network systems of modest size can perform well on tasks calibrated to the size of “brain”. Traffic light controllers perform a critical task in modern society and represent a technical challenge well within capabilities of emerging neural network technology.
Practical solid-state devices currently in production provide economic solutions to many real-world problems. Current and proposed solid-state technologies, such as flash memory and memristor devices, offer very high density analog nonvolatile storage elements well-suited to constructing high-density solid-state analog neural network devices. Typical flash memory arrays of 4 billion transistors or more in size could yield neural network devices with 4 million or more neurons. While the human brain is capable of incredibly complex pattern recognition and prediction, artificial intelligence systems with far fewer neurons can accomplish important real world tasks. Current implementations of solid-state neural network devices have bridged the gap between human and artificial neural network capacities with techniques such as reducing size of input data streams with integrated digital signal processing (DSP) modules.
Artificial analog neural networks have recognition engines typically employ K-Nearest Neighbor (KNN) or radial basis function (RBF) nonlinear classifiers or both. While KNN classification is useful in applications seeking merely the closest match to a recognized pattern, RBF classification is particularly useful in traffic control applications by virtue of its “yes, no, or uncertain” output states. Current solid-state devices, in addition to having such DSP and classification modules, can also be interconnected for scaling to larger, multi-level neural networks. As such, aspects of the present innovations may be implemented with existing technology or with future devices having larger neuron capacity or with future, fully integrated solid-state devices having all the capability herein described.
Certain advantages of the systems and methods herein are obvious to readers having personal experience with automobile travel. Among other things, current vehicle sensors provide inadequate recognition of incoming traffic, forcing traffic to stop before traffic signal light changes are effected. Or, as will be shown, sensors intended to provide advance traffic flow information cannot fully comprehend driver intentions, resulting in incorrect decisions by current traffic light control systems. While hybrid automotive technology improves efficiency by capturing energy otherwise lost when vehicles are stopped, it is even more efficient to regulate traffic flow to maximize overall throughput, minimizing cumulative vehicle wait times. Vehicle wait times equal passenger wait times, such that improved traffic flow yields both improvements in overall fuel economy and increased driver productivity. Innovations herein include systems capable of providing comprehensive traffic control system functionality, such as full implementation of various features and aspects.
Further aspects consistent with the present innovations relate to overall system performance due to real-time, parallel recognition and traffic flow decision selection by the neural network. As with a human observer or traffic officer, a comprehensive overall picture of traffic flow needs yields an immediate decision for optimal traffic flow through an intersection. As such, according to some implementations, intermediate hierarchical layers of the neural network aggregate such an overall picture from which a specific, optimal traffic light sequence is selected. Performance of significantly higher magnitude than existing systems may be achieved via a fully integrated solid-state device implementation of the system, surpassing that of algorithmic implementations using digital processors.